Systems and methods for outbound forecasting based on a fulfillment center priority value

Abstract

The embodiments of the present disclosure provide systems and methods for outbound forecasting, comprising receiving an initial distribution of priority values to each fulfillment center (FC) in each region, running a simulation, using a simulation algorithm, of the initial distribution, calculating an outbound capacity utilization value of each FC, determining a number of FCs comprising an outbound capacity utilization value that exceeds a predetermined threshold, feeding the simulation algorithm with the determined number of FCs to generate one or more additional distributions of priority values, generating a FC priority filter comprising an optimal set of priority values based on the one or more additional distributions of priority values, and modifying an allocation of a plurality of SKUs among a plurality of FCs based on the generated FC priority filter.

What is claimed is: 
     
       1. A computer-implemented system for outbound forecasting, the system comprising:
 a memory storing instructions; and 
 at least one processor configured to execute the instructions to:
 receive an initial distribution of priority values to each fulfillment center (FC) in each region; 
 run a simulation, using a genetic algorithm, of the initial distribution; 
 calculate an outbound capacity utilization value of each FC; 
 determine a number of FCs comprising an outbound capacity utilization value that exceeds a first predetermined threshold; 
 feed the genetic algorithm with at least one of the determined number of FCs to generate one or more additional distributions of priority values; 
 generate a FC priority filter based on the one or more additional distributions of priority values; 
 wherein the generated FC priority filter comprises an optimal matrix of priority values such that the number of FCs, having the outbound capacity utilization value greater than the first predetermined threshold, exceeds a second predetermined threshold, 
 simulate customer demand at each of the plurality of FCs; and 
 wherein products associated with the plurality of SKUs are stowed in corresponding FCs based on the allocation for shipping to customers, 
 modify an allocation of a plurality of SKUs among a plurality of FCs based on the generated FC priority filter and the simulated customer demand, 
 wherein the at least one processor is further configured to execute the instructions to cache at least a portion of the simulation algorithm, and 
 wherein the cached portion of the simulation algorithm comprises at least one constraint that remains substantially constant with each run of the simulation algorithm. 
 
 
     
     
       2. The system of  claim 1 , wherein the predetermined threshold comprises a minimum outbound of each FC. 
     
     
       3. The system of  claim 1 , wherein the outbound capacity utilization value of each FC comprises a ratio of an outbound of each FC to an outbound capacity of each FC. 
     
     
       4. The system of  claim 1 , wherein each of the initial distribution and the one or more additional distributions comprises a two-dimensional matrix of priority values. 
     
     
       5. The system of  claim 1 , wherein the initial distribution of priority values to each FC in each region is randomly generated. 
     
     
       6. The system of  claim 1 , wherein the simulation algorithm comprises at least one constraint, the constraint comprising at least one of customer demand at each of the FCs, maximum capacities of the FCs, compatibility with FCs, or transfer costs between FCs. 
     
     
       7. The system of  claim 1 , wherein each of the plurality of SKUs is indicative of at least one of a manufacturer, material, size, color, packaging, type, or weight of a product. 
     
     
       8. A computer-implemented method for outbound forecasting, the method comprising:
 receiving an initial distribution of priority values to each fulfillment center (FC) in each region; 
 running a simulation, using a genetic algorithm, of the initial distribution; 
 calculating an outbound capacity utilization value of each FC; 
 determining a number of FCs comprising an outbound capacity utilization value that exceeds a predetermined threshold; 
 feeding the genetic algorithm with the determined number of FCs to generate one or more additional distributions of priority values; 
 generating a FC priority filter based on the one or more additional distributions of priority values; 
 wherein the generated FC priority filter comprises an optimal matrix of priority values such that the number of FCs having the outbound capacity utilization value greater than the first predetermined threshold exceeds a second predetermined threshold, 
 simulating customer demand at each of the plurality of FCs; and 
 wherein products associated with the plurality of SKUs are stowed in corresponding FCs based on the allocation for shipping to customers, 
 modifying an allocation of a plurality of SKUs among a plurality of FCs based on the generated FC priority filter and the simulated customer demand, 
 wherein the method further comprises caching at least a portion of the simulation algorithm, and 
 wherein the cached portion of the simulation algorithm comprises at least one constraint that remains substantially constant with each run of the simulation algorithm. 
 
     
     
       9. The method of  claim 8 , wherein the predetermined threshold comprises a minimum outbound of each FC. 
     
     
       10. The method of  claim 8 , wherein the outbound capacity utilization value of each FC comprises a ratio of an outbound of each FC to an outbound capacity of each FC. 
     
     
       11. The method of  claim 8 , wherein each of the initial distribution and the one or more additional distributions comprises a two-dimensional matrix of priority values. 
     
     
       12. The method of  claim 8 , wherein the initial distribution of priority values to each FC in each region is randomly generated. 
     
     
       13. The method of  claim 8 , wherein the simulation algorithm comprises at least one constraint, the constraint comprising at least one of customer demand at each of the FCs, maximum capacities of the FCs, compatibility with FCs, or transfer costs between FCs. 
     
     
       14. A computer-implemented system for outbound forecasting, the system comprising:
 a memory storing instructions; and 
 at least one processor configured to execute the instructions to: 
 receive an initial matrix of priority values to each fulfillment center (FC) in each region, wherein the initial matrix is randomly generated; 
 run a simulation, using a genetic algorithm, of the initial matrix; 
 calculate an outbound capacity utilization value of each FC, wherein the outbound capacity utilization value of each FC comprises a ratio of an outbound of each FC to an outbound capacity of each FC; 
 determine a number of FCs comprising an outbound capacity utilization value that exceeds a predetermined threshold; 
 feed the genetic algorithm with the determined number of FCs to generate one or more additional matrices of priority values; 
 generate a FC priority filter based on the one or more additional matrices of priority values; 
 wherein the generated FC priority filter comprises an optimal matrix of priority values such that the number of FCs having the outbound capacity utilization value greater than the first predetermined threshold, exceeds a second predetermined threshold, 
 simulate customer demand at each of the plurality of FCs; and 
 wherein products associated with the plurality of SKUs are stowed in corresponding FCs based on the allocation for shipping to customers, 
 modify an allocation of a plurality of SKUs among a plurality of FCs based on the generated FC priority filter and the simulated customer demand, 
 wherein the at least one processor is further configured to execute the instructions to cache at least a portion of the simulation algorithm, and 
 wherein the cached portion of the simulation algorithm comprises at least one constraint that remains substantially constant with each run of the simulation algorithm.